Random vibration testing has been around for quite some time. But traditional random vibration testing is Gaussian in nature. The averaging method used in random vibration testing produces a Gaussian distribution of data in which the highest peak accelerations are approximately +/- 3 times the average acceleration. Gaussian distribution has been universally accepted as a legitimate averaging technique, in part, because it has been assumed that “real-world” data is Gaussian.
However, several studies have determined that “real-world” data in actually non-Gaussian. Since this is true, the question is how can one make a random vibration test more realistic?
What is Gaussian Distribution?
With the use of statistics, one can find a number of interesting things about a set of collected data. For example, one can easily compute the mean and the standard deviation of a data set – statistical concepts familiar to most people communicating the average of the data set and the range in which most of the data points fall. But a less familiar statistical concept is the kurtosis of the data set. Kurtosis is a measure of the “peakyness” of the probability distribution of the data. For example, a high kurtosis value indicates the data is distributed with some very large outlier data points, while a low kurtosis value indicates most data points fall near the mean with few and small outlier data points.
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If one evaluates a random data set, one will find that the data points will fall within +/- 3 standard deviations of the average (mean) (Figure 1). This is defined as a Gaussian distribution (kurtosis = 3).